Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm - Van Tran, Mien; Ly, Duy-Khuong; Nguyen, Tan; Tran, Nhi (Construction & building materials, 2024)
This study addresses the growing interest in utilizing steel slag as a sustainable alternative to river sand in additive manufacturing of concrete, driven by the increasing scarcity of natural resources. The rheological properties of fresh material significantly impact the quality of 3D-printed filament, necessitating suitable workability for printability. The research focuses on evaluating the influence of steel slag aggregate and key admixtures, such as silica fume and superplasticizer, on the workability properties of fresh concrete. Through an extensive series of 90 slump tests, optimal combinations ensuring the desired workability for 3D printing applications were identified. To enhance the manufacturing of fresh concrete and develop user-friendly tools, a novel soft computing approach is introduced—Adaptive Elitist Differential Evolution coupled with Bayesian Regularization Artificial Neural Network (aeDE-BRANN). This advanced model considers five critical input parameters: silica fume to Portland cement ratio, steel slag to cement ratio, water to cement ratio, cement content, and superplasticizer dosage. The model outputs crucial workability metrics, including slump flow and slump. In a comprehensive comparative study, the aeDE-BRANN framework demonstrates superior performance in terms of both simplicity and efficiency when compared to other forecasting models. Feature analysis techniques, including Shapley values from game theory and partial dependence plots, provide valuable insights into the intricate relationships between input variables and workability properties. The findings underscore the water to cement ratio as the most influential factor on workability, followed by silica fume to Portland cement ratio and steel slag to cement ratio. This study contributes a reliable tool for predicting workability properties, aiding in the selection of suitable steel slag aggregate and admixtures. Ultimately, it facilitates the sustainable and innovative evolution of 3D concrete printing practices. •aeDE-BRNN Excels in 3D Printing Prediction: Outperforms XGB, DT, SVM, and GD-ANN, enabling practical decisions.•Critical Workability Factors Revealed: SHAP values and Partial Dependence Plots identify key determinants.•Holistic Integration Drives Innovation: aeDE-BRNN, feature analysis, and PDPs synergize for advancements.•Precise Material Optimization: Steel slag aggregate integration and model insights enable enhanced efficiency.